Results 11 to 20 of about 4,584 (156)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014 Fracture is the main pore space for volcanic reservoir, serving as the controlling factor of reservoir productivity. Conventional well logging data often fail to fracture characterization and classification in volcanic reservoir since the degree or extent of the fracture development varies in scales in different locations.
Xinmin Ge +4 more
openaire +3 more sources
IEEE Access, 2020 Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels can capture data nonlinearity by projecting the original variable space to a high-dimensional kernel feature space and obtaining the kernel principal components.
Ruomu Tan +2 more
doaj +1 more source
Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes
IEEE Access, 2022 Fault detection is necessary for safe operation in modern process plants. The kernel principal component analysis (KPCA) technique has been widely utilized for monitoring non-linear processes because it enhances dimension reduction and fault detection in
K. Ramakrishna Kini +2 more
doaj +1 more source
A kernel Principal Component Analysis (kPCA) Digest with a New Backward Mapping (pre-image reconstruction) Strategy [PDF]
, 2020 Abstract Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible dimensionality reduction if data belong to a nonlinear low-dimensional manifold.
Alberto García-González +3 more
openaire +2 more sources
Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics
International Journal of Chemical Engineering, 2022 Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a ...
Chenxing Xu +4 more
doaj +1 more source
Nonlinear process fault detection and identification using kernel PCA and kernel density estimation [PDF]
, 2016 Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance.
Cao, Yi, Samuel, Raphael
core +1 more source
Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring
IEEE Access, 2019 Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high ...
Lingling Guo +3 more
doaj +1 more source
Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach [PDF]
, 2020 In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction.
casella +9 more
core +2 more sources
KOMBINASI KPCA DAN EUCLIDEAN DISTANCE UNTUK PENGENALAN CITRA WAJAH
Rekayasa, 2011 Permasalahan machine learning dan pattern recognition bukan merupakan penelitian yang baru. Seiring dengan perkembangan teknologi, semakin berkembang pula teknik dan algoritma yang digunakan untuk menyelesaikan permasalahan machine learning dan pattern ...
Rima Tri Wahyuningrum
doaj +1 more source
Applied Sciences, 2021 Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of ...
Yingying Fan +4 more
doaj +1 more source